Abstract 4364857: Retrospective Analysis of the Accuracy and Clinical Utility of Predictive Artificial Intelligence in Cardiovascular Event Risk Assessment : PACE Study

Effects of Increasing High-Density Lipoprotein Cholesterol and Decreasing Low-Density Lipoprotein Cholesterol on the Incidence of First Acute Coronary Events (from the Air Force/Texas Coronary Atherosclerosis Prevention Study)

Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices

The Clinical Efficacy Assessment Subcommittee of the American College of Physicians–American Society of Internal Medicine acknowledges the scientific validity of this product as a background paper and as a review that captures the levels of evidence in the management of patients with chronic stable angina as of November 17, 2002. The American College of Cardiology (ACC)/American Heart Association (AHA) Task Force on Practice Guidelines regularly reviews existing guidelines to determine when an update or a full revision is needed. This process gives priority to areas in which major changes in text, and particularly recommendations, are merited on the basis of new understanding or evidence. Minor changes in verbiage and references are discouraged. The ACC/AHA/American College of Physicians–American Society of Internal Medicine (ACP-ASIM) Guidelines for the Management of Patients With Chronic Stable Angina, which were published in June 1999, have now been updated. The full-text guideline incorporating the updated material is available on the Internet (www.acc.org or www.americanheart.org) in both a track-changes version showing the changes in the 1999 guideline in strike-out (deleted text) and highlighting …

Polymorphisms Associated With Cholesterol and Risk of Cardiovascular Events

Conclusions from the VA-HIT study

Deep learning-based LDL-C level prediction and explainable AI interpretation.

Accurate forecasting of agricultural commodity prices is essential for market planning and policy formulation, especially in agriculture-dependent economies like India. Price volatility, driven by factors such as weather variability and market demand fluctuations, poses significant forecasting challenges. This study evaluates the performance of traditional stochastic models, machine learning techniques, and deep learning approaches in forecasting the prices of 23 commodities using daily wholesale price data from January 2010 to June 2024. Models assessed include Autoregressive Integrated Moving Average, Support Vector Regression, Extreme Gradient Boosting, Multilayer Perceptron, Recurrent Neural Networks, Long Short-Term Memory Networks, Gated Recurrent Units, and Echo State Networks. Results show that deep learning models, particularly Long Short-Term Memory and Gated Recurrent Units, outperform others in capturing complex temporal patterns, achieving superior accuracy across error metrics. The results indicate that deep learning models, particularly Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU), demonstrate superior performance in capturing complex temporal patterns. For instance, the GRU model achieved a Root Mean Squared Error (RMSE) of 369.54 for onions and 210.35 for tomatoes, significantly outperforming the ARIMA model, which recorded RMSE values of 1564.62 and 1298.60, respectively. Furthermore, the Mean Absolute Percentage Error (MAPE) for GRU was notably lower, at 14.59% for onions and 10.58% for tomatoes. These results underscore the efficacy of deep learning approaches in addressing the inherent volatility and nonlinear dynamics of agricultural commodity prices. These findings offer valuable insights for policymakers, traders, and farmers, enabling better market interventions, crop planning, and risk management. The study recommends exploring hybrid models and incorporating external factors like weather data to further enhance forecasting reliability.

PurposeThe purpose of this study is to develop and test a new deep learning model to predict aircraft fuel consumption. For this purpose, real data obtained from different landings and take-offs were used. As a result, a new hybrid convolutional neural network (CNN)-bi-directional long short term memory (BiLSTM) model was developed as intended.Design/methodology/approachThe data used are divided into training and testing according to the k-fold 5 value. In this study, 13 different parameters were used together as input parameters. Fuel consumption was used as the output parameter. Thus, the effect of many input parameters on fuel flow was modeled simultaneously using the deep learning method in this study. In addition, the developed hybrid model was compared with the existing deep learning models long short term memory (LSTM) and BiLSTM.FindingsIn this study, when tested with LSTM, one of the existing deep learning models, values of 0.9162, 6.476, and 5.76 were obtained for R2, root mean square error (RMSE), and mean absolute percentage error (MAPE), respectively. For the BiLSTM model when tested, values of 0.9471, 5.847 and 4.62 were obtained for R2, RMSE and MAPE, respectively. In the proposed hybrid model when tested, values of 0.9743, 2.539 and 1.62 were obtained for R2, RMSE and MAPE, respectively. The results obtained according to the LSTM and BiLSTM models are much closer to the actual fuel consumption values. The error of the models used was verified against the actual fuel flow reports, and an average absolute percent error value of less than 2% was obtained.Originality/valueIn this study, a new hybrid CNN-BiLSTM model is proposed. The proposed model is trained and tested with real flight data for fuel consumption estimation. As a result of the test, it is seen that it gives much better results than the LSTM and BiLSTM methods found in the literature. For this reason, it can be used in many different engine types and applications in different fields, especially the turboprop engine used in the study. Because it can be applied to different engines than the engine type used in the study, it can be easily integrated into many simulation models.

This study focuses on the use of deep learning and machine learning models to forecast cow cheese production in Turkey. In particular, our research utilizes the LSTM (long short-term memory) model to forecast cow cheese production for the next 12 months by extensively utilizing deep learning and machine learning techniques that have not been applied in this field before. In addition to LSTM, models such as GRU (Gated Recurrent Unit), MLP (Multi-Layer Perceptron), SVR (Support Vector Regression), and KNN (K-Nearest Neighbors) were also tested, and their performances were compared using RMSE (Root Mean Square Error), MSE (Mean Squared Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and (Coefficient of Determination) metrics. The findings revealed that the LSTM model performed significantly better than the other models in terms of RMSE, MSE, MAE, and MAPE values. This result indicates that the LSTM model provides high accuracy and reliability in forecasting cow cheese production. This achievement of the model offers important applications in areas such as supply chain management, inventory optimization, and demand forecasting in the dairy industry.

To determine the relative contributions of triglycerides (TGs) and high-density lipoprotein (HDL) cholesterol in the residual risk of coronary heart disease (CHD) after the reduction of low-density lipoprotein (LDL) cholesterol to guideline-recommended levels, we conducted a hospital-based, case-control study with optimal matching in the strata of LDL cholesterol, gender, ethnicity, and age. The 170 cases and 175 controls were patients at Brigham and Women's Hospital (Boston, Massachusetts) from 2005 to 2008 who had an LDL cholesterol level <130 mg/dl. The cases had incident CHD, and the controls had diagnoses unrelated to CHD. The 170 cases and 175 controls had a mean LDL cholesterol level of 73 and 87 mg/dl, respectively. The association between TG and HDL cholesterol levels and CHD risk was assessed using conditional and unconditional logistic regression analysis. The models investigated accommodated the possibility of an interaction between lipid factors. The odds of CHD increased by approximately 20% per 23-mg/dl increase in TGs and decreased by approximately 40% per 7.5-mg/dl decrease in HDL cholesterol. High TGs and low HDL cholesterol interacted synergistically to increase the odds ratio to 10 for the combined greatest TG (> or =190 mg/dl) and lowest HDL cholesterol quintiles (<30 mg/dl). High TG levels were more strongly associated with CHD when the HDL cholesterol was low than average or high; and low HDL cholesterol levels were more strongly associated with CHD when the TGs were high. TGs and HDL cholesterol were associated with CHD in patients with a LDL cholesterol level of < or =70 mg/dl, with a risk similar to, or greater than, those in the total group. In conclusion, high TG and low HDL cholesterol levels contribute strongly and synergistically to CHD when LDL cholesterol is well controlled. Thus, high TGs might have greater importance in patients with optimal rather than greater LDL cholesterol concentrations.

Forecasting cryptocurrency prices using Recurrent Neural Network and Long Short-term Memory

Purpose: The aim of the research is to utilize a hybrid model that combines the linear model represented by Autoregressive Distributed Lag (ARDL) and the nonlinear model represented by deep learning models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). Theoretical Framework: The theoretical framework integrates the linear component represented by the Autoregressive Distributed Lag (ARDL) model and the nonlinear component represented by deep learning models, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) To create hybrid models. Design/Methodology/Approach: The research methodology involves the use of EVIEWS 12 for analyzing standard data and integrating the ARDL model, as well as Python programming for building the proposed forecasting models. Weekly data from the Iraqi stock market, specifically from the banking and communications sectors spanning from 2017 to 2021, is utilized. The study compares the performance of the hybrid models ARDL_LSTM and ARDL_GRU with individual models using evaluation metrics such as root mean square error (RMSE) and mean absolute percentage error (MAPE). Findings: The results indicate the superiority of the hybrid model ARDL_LSTM over other models due to its high accuracy and lower comparison measurement values. Originality/Value: The originality of the research lies in its hybrid approach, combining ARDL with deep learning models like LSTM and GRU for time series forecasting. This approach adds value by addressing the limitations of individual models and improving forecasting accuracy in the context of the Iraqi stock market.

BackgroundMore than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country’s policy measures.ObjectiveWe aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries.MethodsThe COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set.ResultsA total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea.ConclusionsThe CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning–based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

The primary objective of this study is to analyze multivariate time series data by employing the Long Short-Term Memory (LSTM) model. Deep learning models often face issues when dealing with multivariate time series data, which is defined by several variables that have diverse value ranges. These challenges arise owing to the potential biases present in the data. In order to tackle this issue, it is crucial to employ normalization techniques such as min-max and z-score to guarantee that the qualities are standardized and can be compared effectively. This study assesses the effectiveness of the LSTM model by applying two normalizing techniques in five distinct attribute selection scenarios. The aim of this study is to ascertain the normalization strategy that produces the most precise outcomes when employed in the LSTM model for the analysis of multivariate time series. The evaluation measures employed in this study comprise Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-Squared (R2). The results suggest that the min-max normalization method regularly yields superior outcomes in comparison to the z-score method. Min-max normalization specifically resulted in a decreased mean absolute percentage error (MAPE) and root mean square error (RMSE), as well as an increased R-squared (R2) value. These improvements indicate enhanced accuracy and performance of the model. This paper makes a significant contribution by doing a thorough comparison analysis of normalizing procedures. It offers vital insights for researchers and practitioners in choosing suitable preprocessing strategies to improve the performance of deep learning models. The study's findings underscore the importance of selecting the appropriate normalization strategy to enhance the precision and dependability of multivariate time series predictions using LSTM models. To summarize, the results indicate that min-max normalization is superior to z-score normalization for this particular use case. This provides a useful suggestion for further studies and practical applications in the field. This study emphasizes the significance of normalization in analyzing multivariate time series and contributes to the larger comprehension of data preprocessing in deep learning models

Associations of oral contraceptive use with serum lipids and lipoproteins in young women: The Bogalusa Heart Study